Paper summaryqureai3D Image Classification is one of the utmost important requirements in healthcare. But due to huge size of such images and less amount of data, it has become difficult to train traditional end-to-end models of classification such as AlexNet, Resnet, VGG. Major approaches considered for 3D images use slices of the image and not the image as whole. This approach at times leads to loss of information across third axis.
The paper uses the complete 3D image for training, and since the number of images are less we use unsupervised techniques for weights initialization and fine tune the fully connected layers using supervised learning for classification. The major contribution of the paper is the extension of 2D Convolutional Auto-encoders to 3D Domain and taking it further for MRI Classification which can be useful in numerous other applications.
**Algorithm**
1. Train 3D Convolution Auto-encoders using [CADDementia dataset](https://grand-challenge.org/site/caddementia/home/)
2. Extract the encoder layer from the trained auto-encoders and add fully connected layers to it, followed by softmax
3. Train and test the classification model using [ADNI data](http://adni.loni.usc.edu/)
**3D CAN Model**
This model is 3D extension of [2D Convolutional Auto-encoders](http://people.idsia.ch/~ciresan/data/icann2011.pdf) (2D CAN)
![3D Convolutional Auto-encoders](http://i.imgur.com/66y52uQ.png)
Each convolution in encoder is a 3D Convolution followed by ReLU and maxpool, while in decoder, each convolution is a maxunpool followed by 3D Full Convolution and ReLU. In the last decoder block, instead of ReLU, Sigmoid will be used.
Here the weights were shared in the decoder and encoder as specified in [2D CAN](http://people.idsia.ch/~ciresan/data/icann2011.pdf)
**3D Classification model**
![Classification of MRIs using 3D Convolutional Auto-encoders](http://i.imgur.com/3hOeR8o.png)
The weights of the classification model are initialized using the trained 3D-CAN model as mentioned in algorithm.
**Loss Function**
Weighted negative log likelihood for Classification and Mean Squared Error for Unsupervised learning
**Training algorithm**
[Adadelta](https://arxiv.org/abs/1212.5701)
**Results**
Obtained 89.1% on 10-fold cross validation on dataset of 270 patients for classification of MRI into Mild Cognitive Impairment (MCI), Normal Control (NC) and Alzheimer's disease (AD)
**Image Source**
All images are taken from the [paper](https://arxiv.org/abs/1607.00455) itself.

3D Image Classification is one of the utmost important requirements in healthcare. But due to huge size of such images and less amount of data, it has become difficult to train traditional end-to-end models of classification such as AlexNet, Resnet, VGG. Major approaches considered for 3D images use slices of the image and not the image as whole. This approach at times leads to loss of information across third axis.
The paper uses the complete 3D image for training, and since the number of images are less we use unsupervised techniques for weights initialization and fine tune the fully connected layers using supervised learning for classification. The major contribution of the paper is the extension of 2D Convolutional Auto-encoders to 3D Domain and taking it further for MRI Classification which can be useful in numerous other applications.
**Algorithm**
1. Train 3D Convolution Auto-encoders using [CADDementia dataset](https://grand-challenge.org/site/caddementia/home/)
2. Extract the encoder layer from the trained auto-encoders and add fully connected layers to it, followed by softmax
3. Train and test the classification model using [ADNI data](http://adni.loni.usc.edu/)
**3D CAN Model**
This model is 3D extension of [2D Convolutional Auto-encoders](http://people.idsia.ch/~ciresan/data/icann2011.pdf) (2D CAN)
![3D Convolutional Auto-encoders](http://i.imgur.com/66y52uQ.png)
Each convolution in encoder is a 3D Convolution followed by ReLU and maxpool, while in decoder, each convolution is a maxunpool followed by 3D Full Convolution and ReLU. In the last decoder block, instead of ReLU, Sigmoid will be used.
Here the weights were shared in the decoder and encoder as specified in [2D CAN](http://people.idsia.ch/~ciresan/data/icann2011.pdf)
**3D Classification model**
![Classification of MRIs using 3D Convolutional Auto-encoders](http://i.imgur.com/3hOeR8o.png)
The weights of the classification model are initialized using the trained 3D-CAN model as mentioned in algorithm.
**Loss Function**
Weighted negative log likelihood for Classification and Mean Squared Error for Unsupervised learning
**Training algorithm**
[Adadelta](https://arxiv.org/abs/1212.5701)
**Results**
Obtained 89.1% on 10-fold cross validation on dataset of 270 patients for classification of MRI into Mild Cognitive Impairment (MCI), Normal Control (NC) and Alzheimer's disease (AD)
**Image Source**
All images are taken from the [paper](https://arxiv.org/abs/1607.00455) itself.